R Tutorial
An introduction to R
Introduction
This tutorial is will introduce the reader to
,
a free, open-source statistical computing environment often used with
RStudio, a integrated development environment for
.
R Project Logo
Download
Download at https://www.r-project.org/
Download RStudio at https://rstudio.com/products/rstudio/download/
Calculator
can be used as a super awesome calculator
# 5 + 3 = 8
5 + 3 ## [1] 8
# 24 / (1 + 2) = 8
24 / (1 + 2) ## [1] 8
# 2 * 2 * 2 = 8
2^3 ## [1] 8
# 8 * 8 = 64
sqrt(64) ## [1] 8
# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000) ## [1] 8
Functions
has many useful built in functions
1:10## [1] 1 2 3 4 5 6 7 8 9 10
as.character(1:10)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
rep(1:2, times = 5)## [1] 1 2 1 2 1 2 1 2 1 2
rep(1:5, times = 2)## [1] 1 2 3 4 5 1 2 3 4 5
rep(1:5, each = 2)## [1] 1 1 2 2 3 3 4 4 5 5
rep(1:5, length.out = 7)## [1] 1 2 3 4 5 1 2
seq(5, 50, by = 5)## [1] 5 10 15 20 25 30 35 40 45 50
seq(5, 50, length.out = 5)## [1] 5.00 16.25 27.50 38.75 50.00
paste(1:10, 20:30, sep = "-")## [1] "1-20" "2-21" "3-22" "4-23" "5-24" "6-25" "7-26" "8-27" "9-28" "10-29" "1-30"
paste(1:10, collapse = "-")## [1] "1-2-3-4-5-6-7-8-9-10"
paste0("x", 1:10)## [1] "x1" "x2" "x3" "x4" "x5" "x6" "x7" "x8" "x9" "x10"
min(1:10)## [1] 1
max(1:10)## [1] 10
range(1:10)## [1] 1 10
mean(1:10)## [1] 5.5
sd(1:10)## [1] 3.02765
Custom Functions
Users can also create their own functions
customFunction1 <- function(x, y) {
z <- 100 * x / (x + y)
paste(z, "%")
}
customFunction1(x = 10, y = 90)## [1] "10 %"
customFunction2 <- function(x) {
mymin <- mean(x - sd(x))
mymax <- mean(x) + sd(x)
print(paste("Min =", mymin))
print(paste("Max =", mymax))
}
customFunction2(x = 1:10)## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"
for loops and if else
statements
xx <- NULL #creates and empty object
for(i in 1:10) {
xx[i] <- i*3
}
xx## [1] 3 6 9 12 15 18 21 24 27 30
xx %% 2 #gives the remainder when divided by 2## [1] 1 0 1 0 1 0 1 0 1 0
for(i in 1:length(xx)) {
if((xx[i] %% 2) == 0) {
print(paste(xx[i],"is Even"))
} else {
print(paste(xx[i],"is Odd"))
}
}## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"
# or
ifelse(xx %% 2 == 0, "Even", "Odd")## [1] "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even"
paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))## [1] "3 is Odd" "6 is Even" "9 is Odd" "12 is Even" "15 is Odd" "18 is Even" "21 is Odd" "24 is Even" "27 is Odd" "30 is Even"
Objects
Information can be stored in user defined objects, in multiple forms:
c(): a string of valuesmatrix(): a two dimensional matrix in one formatdata.frame(): a two dimensional matrix where each column can be a different formatlist():
A string…
xc <- 1:10
xc## [1] 1 2 3 4 5 6 7 8 9 10
xc <- c(1,2,3,4,5,6,7,8,9,10)
xc## [1] 1 2 3 4 5 6 7 8 9 10
A matrix…
xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 2 3 4 5 6 7 8 9 10
## [2,] 11 12 13 14 15 16 17 18 19 20
## [3,] 21 22 23 24 25 26 27 28 29 30
## [4,] 31 32 33 34 35 36 37 38 39 40
## [5,] 41 42 43 44 45 46 47 48 49 50
## [6,] 51 52 53 54 55 56 57 58 59 60
## [7,] 61 62 63 64 65 66 67 68 69 70
## [8,] 71 72 73 74 75 76 77 78 79 80
## [9,] 81 82 83 84 85 86 87 88 89 90
## [10,] 91 92 93 94 95 96 97 98 99 100
xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
A data frame…
xd <- data.frame(
x1 = c("aa","bb","cc","dd","ee",
"ff","gg","hh","ii","jj"),
x2 = 1:10,
x3 = c(1,1,1,1,1,2,2,2,3,3),
x4 = rep(c(1,2), times = 5),
x5 = rep(1:5, times = 2),
x6 = rep(1:5, each = 2),
x7 = seq(5, 50, by = 5),
x8 = log10(1:10),
x9 = (1:10)^3,
x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
A list…
xl <- list(xc, xm, xd)
xl[[1]]## [1] 1 2 3 4 5 6 7 8 9 10
xl[[2]]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
xl[[3]]## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
Selecting Data
xc[5] # 5th element in xc## [1] 5
xd$x3[5] # 5th element in col "x3"## [1] 1
xd[5,"x3"] # row 5, col "x3"## [1] 1
xd$x3 # all of col "x3"## [1] 1 1 1 1 1 2 2 2 3 3
xd[,"x3"] # all rows, col "x3"## [1] 1 1 1 1 1 2 2 2 3 3
xd[3,] # row 3, all cols## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"## x4 x5
## 2 2 2
## 4 2 4
xl[[3]]$x1 # 3rd object in the list, col "x1## [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"
regexpr
xx <- data.frame(Name = c("Item 1 (detail 1)",
"Item 20 (detail 20)",
"Item 300 (detail 300)"),
Item = NA,
Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx## Name Item Detail
## 1 Item 1 (detail 1) Item 1 detail 1
## 2 Item 20 (detail 20) Item 20 detail 20
## 3 Item 300 (detail 300) Item 300 detail 300
Data Formats
Data can also be saved in many formats:
- numeric
- integer
- character
- factor
- logical
xd$x3 <- as.character(xd$x3)
xd$x3## [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"
xd$x3 <- as.numeric(xd$x3)
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
xd$x3 <- as.factor(xd$x3)
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3
xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1
xd$x10## [1] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
as.numeric(xd$x10) # TRUE = 1, FALSE = 0## [1] 1 1 1 0 0 1 1 0 0 0
sum(xd$x10)## [1] 5
Internal structure of an object can be checked with
str()
str(xc) # c()## num [1:10] 1 2 3 4 5 6 7 8 9 10
str(xm) # matrix()## int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
str(xd) # data.frame()## 'data.frame': 10 obs. of 10 variables:
## $ x1 : chr "aa" "bb" "cc" "dd" ...
## $ x2 : int 1 2 3 4 5 6 7 8 9 10
## $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
## $ x4 : num 1 2 1 2 1 2 1 2 1 2
## $ x5 : int 1 2 3 4 5 1 2 3 4 5
## $ x6 : int 1 1 2 2 3 3 4 4 5 5
## $ x7 : num 5 10 15 20 25 30 35 40 45 50
## $ x8 : num 0 0.301 0.477 0.602 0.699 ...
## $ x9 : num 1 8 27 64 125 216 343 512 729 1000
## $ x10: logi TRUE TRUE TRUE FALSE FALSE TRUE ...
str(xl) # list()## List of 3
## $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
## $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
## $ :'data.frame': 10 obs. of 10 variables:
## ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
## ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
## ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
## ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
## ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
## ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
## ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
## ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
## ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
## ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...
Packages
Additional libraries can be installed and loaded for use.
install.packages("scales")library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx## Values Rescaled
## 1 1 1.000000
## 2 2 4.222222
## 3 3 7.444444
## 4 4 10.666667
## 5 5 13.888889
## 6 6 17.111111
## 7 7 20.333333
## 8 8 23.555556
## 9 9 26.777778
## 10 10 30.000000
libraries can also be used without having to load them
scales::rescale(1:10, to = c(1,30))## [1] 1.000000 4.222222 7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778 30.000000
Data Wrangling
R for Data Science - https://r4ds.had.co.nz/
xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
Data1 = 1:10,
Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 X 6 60 66 6000
## 7 X 7 70 77 7000
## 8 X 8 80 88 8000
## 9 Y 9 90 99 9000
## 10 Y 10 100 110 10000
xx$Data1 < 5 # which are less than 5## [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
xx[xx$Data1 < 5,]## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx[xx$Group == "X", c("Group","Data2","NewData1")]## Group Data2 NewData1
## 1 X 10 11
## 2 X 20 22
## 6 X 60 66
## 7 X 70 77
## 8 X 80 88
Data wrangling with tidyverse and pipes
(%>%)
library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
mutate(Data1 = 1:10,
Data2 = seq(10, 100, by = 10),
NewData1 = Data1 + Data2,
NewData2 = Data1 * 1000)
xx## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 Y 6 60 66 6000
## 7 Y 7 70 77 7000
## 8 X 8 80 88 8000
## 9 X 9 90 99 9000
## 10 X 10 100 110 10000
filter(xx, Data1 < 5)## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx %>% filter(Data1 < 5)## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx %>% filter(Group == "X") %>%
select(Group, NewColName=Data2, NewData1)## Group NewColName NewData1
## 1 X 10 11
## 2 X 20 22
## 3 X 80 88
## 4 X 90 99
## 5 X 100 110
xs <- xx %>%
group_by(Group) %>%
summarise(Data2_mean = mean(Data2),
Data2_sd = sd(Data2),
NewData2_mean = mean(NewData2),
NewData2_sd = sd(NewData2))
xs## # A tibble: 2 × 5
## Group Data2_mean Data2_sd NewData2_mean NewData2_sd
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 X 60 41.8 6000 4183.
## 2 Y 50 15.8 5000 1581.
xx %>% left_join(xs, by = "Group")## Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1 X 1 10 11 1000 60 41.83300 6000 4183.300
## 2 X 2 20 22 2000 60 41.83300 6000 4183.300
## 3 Y 3 30 33 3000 50 15.81139 5000 1581.139
## 4 Y 4 40 44 4000 50 15.81139 5000 1581.139
## 5 Y 5 50 55 5000 50 15.81139 5000 1581.139
## 6 Y 6 60 66 6000 50 15.81139 5000 1581.139
## 7 Y 7 70 77 7000 50 15.81139 5000 1581.139
## 8 X 8 80 88 8000 60 41.83300 6000 4183.300
## 9 X 9 90 99 9000 60 41.83300 6000 4183.300
## 10 X 10 100 110 10000 60 41.83300 6000 4183.300
Read/Write data
xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)For excel sheets, the package readxl can be used to read
in sheets of data.
library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")Tidy Data
Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html
Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html
yy <- xx %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = round(mean(DTF),1)) %>%
arrange(Location)
yy## # A tibble: 9 × 3
## # Groups: Name [3]
## Name Location Mean_DTF
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
## <chr> <dbl> <dbl> <dbl>
## 1 CDC Maxim AGL 86.7 134. 52.5
## 2 ILL 618 AGL 79.3 138. 47
## 3 Laird AGL 76.8 137. 56.8
yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy## # A tibble: 9 × 3
## # Groups: Name [3]
## Name TraitName Value
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
yy <- yy %>% spread(key = Name, value = Value)
yy## # A tibble: 3 × 4
## TraitName `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
## <chr> <dbl> <dbl> <dbl>
## 1 Jessore, Bangladesh 86.7 79.3 76.8
## 2 Metaponto, Italy 134. 138. 137.
## 3 Saskatoon, Canada 52.5 47 56.8
Base Plotting
We will start with some basic plotting using the base function
plot()
Tutorial 1 - http://www.sthda.com/english/wiki/r-base-graphs
Tutorial 2 - https://bookdown.org/rdpeng/exdata/the-base-plotting-system-1.html
# A basic scatter plot
plot(x = xd$x8, y = xd$x9)# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)# Adjust plot type
plot(x = xd$x8, y = xd$x9, type = "line")# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")Now lets create some random and normally distributed data to make some more complicated plots
# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru## [1] 66.2356980 48.3884219 5.3633332 91.3236078 32.7618493 33.6759313 59.4240368 70.3702518 42.8220055 80.3577722 69.7771422 98.2763862 12.0832196
## [14] 51.3121011 0.2048701 39.2008766 4.9931458 29.0571271 21.9147889 77.0807889 50.0104219 68.1735076 4.6527507 25.1142512 62.4942887 14.5439188
## [27] 55.7249198 42.8235913 88.6747987 36.5051284 19.3881419 43.7591962 16.7667287 10.3689346 26.1874574 77.4221284 9.9730550 88.4440970 98.4771721
## [40] 40.1377649 63.4408181 85.6825136 99.6875576 70.4390087 80.6781451 96.3686722 44.0955114 9.3909092 13.3748761 64.7724216 77.8164959 60.4842308
## [53] 13.6173409 91.3686662 34.3313301 81.7689981 84.5092433 48.0859720 43.8791090 71.7536593 99.6329408 34.5848317 60.1187158 1.7844257 36.5116244
## [66] 59.6877309 94.2004278 14.7317859 6.3343798 15.2668857 89.5363787 4.8067947 13.6647841 64.1009520 29.3926459 84.2559715 12.0155694 21.4042011
## [79] 94.6187489 1.7350951 58.8863933 97.1692054 32.0448998 49.6989314 18.8252553 12.2145555 88.3973716 89.8489806 15.4399706 78.3297900 39.5121525
## [92] 91.4604050 96.2378172 37.4217201 90.5675797 11.9413448 56.5810436 41.9827503 92.5151631 64.5933928
plot(x = ru)order(ru)## [1] 15 80 64 23 72 17 3 69 48 37 34 96 77 13 86 49 53 73 26 68 70 89 33 85 31 78 19 24 35 18 75 83 5 6 55 62 30
## [38] 65 94 16 91 40 98 9 28 32 59 47 58 2 84 21 14 27 97 81 7 66 63 52 25 41 74 100 50 1 22 11 8 44 60 20 36 51
## [75] 90 10 45 56 76 57 42 87 38 29 71 88 95 4 54 92 99 67 79 93 46 82 12 39 61 43
ru<- ru[order(ru)]
ru## [1] 0.2048701 1.7350951 1.7844257 4.6527507 4.8067947 4.9931458 5.3633332 6.3343798 9.3909092 9.9730550 10.3689346 11.9413448 12.0155694
## [14] 12.0832196 12.2145555 13.3748761 13.6173409 13.6647841 14.5439188 14.7317859 15.2668857 15.4399706 16.7667287 18.8252553 19.3881419 21.4042011
## [27] 21.9147889 25.1142512 26.1874574 29.0571271 29.3926459 32.0448998 32.7618493 33.6759313 34.3313301 34.5848317 36.5051284 36.5116244 37.4217201
## [40] 39.2008766 39.5121525 40.1377649 41.9827503 42.8220055 42.8235913 43.7591962 43.8791090 44.0955114 48.0859720 48.3884219 49.6989314 50.0104219
## [53] 51.3121011 55.7249198 56.5810436 58.8863933 59.4240368 59.6877309 60.1187158 60.4842308 62.4942887 63.4408181 64.1009520 64.5933928 64.7724216
## [66] 66.2356980 68.1735076 69.7771422 70.3702518 70.4390087 71.7536593 77.0807889 77.4221284 77.8164959 78.3297900 80.3577722 80.6781451 81.7689981
## [79] 84.2559715 84.5092433 85.6825136 88.3973716 88.4440970 88.6747987 89.5363787 89.8489806 90.5675797 91.3236078 91.3686662 91.4604050 92.5151631
## [92] 94.2004278 94.6187489 96.2378172 96.3686722 97.1692054 98.2763862 98.4771721 99.6329408 99.6875576
plot(x = ru)# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd## [1] 66.06558 40.98211 57.39567 47.72140 56.10407 68.48810 28.51704 53.37108 35.88500 44.16847 42.46794 65.61741 47.83355 50.70847 74.53826 35.82354
## [17] 38.79670 69.01883 38.99560 48.74034 68.86927 51.39190 33.24321 51.08835 41.44944 53.93695 42.00532 48.81140 46.20535 35.10611 56.45992 49.61530
## [33] 46.18978 57.93760 43.47594 50.74239 57.92178 67.12315 54.72054 53.96038 55.03044 52.06269 59.97764 62.80202 65.74545 36.91012 37.35251 40.61324
## [49] 48.11303 52.32148 55.08260 30.70116 57.41049 48.61337 41.89365 45.78076 52.86590 50.97258 42.99258 53.63298 50.78670 51.08565 41.68910 51.78088
## [65] 60.52888 46.77888 45.21302 55.17283 64.97265 70.60958 51.94254 44.08043 61.94746 47.43291 58.08149 50.79341 48.39256 48.16059 62.75742 26.03647
## [81] 54.75234 45.06448 43.83629 44.42660 65.87691 49.79019 40.64466 54.28714 39.85243 50.56066 57.52121 50.23608 62.14433 46.28311 49.03227 50.94082
## [97] 57.04447 56.05602 41.94674 60.63996
nd <- nd[order(nd)]
nd## [1] 26.03647 28.51704 30.70116 33.24321 35.10611 35.82354 35.88500 36.91012 37.35251 38.79670 38.99560 39.85243 40.61324 40.64466 40.98211 41.44944
## [17] 41.68910 41.89365 41.94674 42.00532 42.46794 42.99258 43.47594 43.83629 44.08043 44.16847 44.42660 45.06448 45.21302 45.78076 46.18978 46.20535
## [33] 46.28311 46.77888 47.43291 47.72140 47.83355 48.11303 48.16059 48.39256 48.61337 48.74034 48.81140 49.03227 49.61530 49.79019 50.23608 50.56066
## [49] 50.70847 50.74239 50.78670 50.79341 50.94082 50.97258 51.08565 51.08835 51.39190 51.78088 51.94254 52.06269 52.32148 52.86590 53.37108 53.63298
## [65] 53.93695 53.96038 54.28714 54.72054 54.75234 55.03044 55.08260 55.17283 56.05602 56.10407 56.45992 57.04447 57.39567 57.41049 57.52121 57.92178
## [81] 57.93760 58.08149 59.97764 60.52888 60.63996 61.94746 62.14433 62.75742 62.80202 64.97265 65.61741 65.74545 65.87691 66.06558 67.12315 68.48810
## [97] 68.86927 69.01883 70.60958 74.53826
plot(x = nd)hist(x = nd)hist(nd, breaks = 20, col = "darkgreen")plot(x = density(nd))boxplot(x = nd)boxplot(x = nd, horizontal = T)ggplot2
Lets be honest, the base plots are ugly! The ggplot2
package gives the user to create a better, more visually appealing
plots. Additional packages such as ggbeeswarm and
ggrepel also contain useful functions to add to the
functionality of ggplot2.
ggplot2 - https://ggplot2.tidyverse.org/
Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html
Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/
The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html
library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()mp + geom_point(aes(color = x3, shape = x3), size = 4)mp + geom_line(size = 2)mp + geom_line(aes(color = x3), size = 2)mp + geom_smooth(method = "loess")mp + geom_smooth(method = "lm")xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
rnorm(50, mean = 60, sd = 5)),
group = factor(rep(1:2, each = 50)),
label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")mp + geom_histogram(color = "black", position = "dodge")mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1mp + geom_density(alpha = 0.5)mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")mp + geom_boxplot() + geom_point()mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")library(ggbeeswarm)
mp + geom_quasirandom()mp + geom_quasirandom(aes(shape = group))mp2 <- mp + geom_violin() +
geom_boxplot(width = 0.1, fill = "white") +
geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
common.legend = T, legend = "bottom")Statistics
Handbook of Biological Statistics - http://biostathandbook.com/
R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html
# Prep data
lev_Loc <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
mutate(Location = factor(Location, levels = lev_Loc),
Name = factor(Name, levels = lev_Name))
xx <- dd %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
## <fct> <dbl> <dbl> <dbl>
## 1 ILL 618 AGL 47 79.3 138.
## 2 CDC Maxim AGL 52.5 86.7 134.
## 3 Laird AGL 56.8 76.8 137.
# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF,
color = Name, group = Name, shape = Name)) +
geom_point(size = 2.5, alpha = 0.7) +
geom_line(size = 1, alpha = 0.7) +
theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.
summary(aov(DTF ~ Name * Location, data = dd))## Df Sum Sq Mean Sq F value Pr(>F)
## Name 2 88 44 3.476 0.0395 *
## Location 2 65863 32931 2598.336 < 2e-16 ***
## Name:Location 4 560 140 11.044 2.52e-06 ***
## Residuals 45 570 13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.
If we only have two things to compare, we could do a t-test.
xx <- dd %>%
filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)##
## Welch Two Sample t-test
##
## data: xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -32.18265 -25.48402
## sample estimates:
## mean of x mean of y
## 52.11111 80.94444
DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.
xx <- dd %>%
filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
Location == "Metaponto, Italy") %>%
spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)##
## Welch Two Sample t-test
##
## data: xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.059739 7.059739
## sample estimates:
## mean of x mean of y
## 137.8333 136.8333
DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.
pch Plot
xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
y = rep(5:1, each = 6, length.out = 26),
pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
geom_point(color = "darkred", fill = "darkblue", size = 5) +
geom_text(aes(label = pch), nudge_x = -0.25) +
scale_shape_manual(values = xx$pch) +
scale_x_continuous(breaks = 6:1) +
scale_y_continuous(breaks = 6:1) +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(title = "Plot symbols in R (pch)",
subtitle = "color = \"darkred\", fill = \"darkblue\"",
x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")R Markdown
Tutorials on how to create an R markdown document like this one can be found here:
- https://rmarkdown.rstudio.com/articles_intro.html
- https://rmarkdown.rstudio.com/lesson-1.html
- https://alexd106.github.io/intro2R/Rmarkdown_intro.html